基于深度学习的高速公路交通流数据监控方法研究

Research on Expressway Traffic Flow Data Monitoring Method Based on Depth Learning

用深度学习的方法对高速公路上的定时交通流进行监测,有关部门根据监测到的数据结果进行交通管控,缓解道路压力,保障交通安全。首先,用递归神经网络RNN进行短期交通流量预测,然后利用残差反卷积形成深层网络从而实现长期的交通流量预测。在此基础上,创新性的使用图卷积进行特征提取,提高模型的鲁棒性。通过实验证明,基于深度学习的长短期交通流量预测具有重要的研究意义和使用价值。

The content of this paper is to monitor the regular traffic flow on expressways with the method of deep learning,and relevant departments carry out traffic control according to the monitored data results to relieve road pressure and ensure traffic safety.First,the recurrent neural network RNN is used for short-term traffic flow prediction,and then the residual deconvolution is used to form a deep network to realize long-term traffic flow prediction.On this basis,the innovative use of graph convolution for feature extraction improves the robustness of the model.Experiments show that the long-term and short-term traffic flow prediction based on depth learning has important research significance and application value.